{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names = [\"duration\",\"protocol_type\",\"service\",\"flag\",\"src_bytes\",\n",
    "    \"dst_bytes\",\"land\",\"wrong_fragment\",\"urgent\",\"hot\",\"num_failed_logins\",\n",
    "    \"logged_in\",\"num_compromised\",\"root_shell\",\"su_attempted\",\"num_root\",\n",
    "    \"num_file_creations\",\"num_shells\",\"num_access_files\",\"num_outbound_cmds\",\n",
    "    \"is_host_login\",\"is_guest_login\",\"count\",\"srv_count\",\"serror_rate\",\n",
    "    \"srv_serror_rate\",\"rerror_rate\",\"srv_rerror_rate\",\"same_srv_rate\",\n",
    "    \"diff_srv_rate\",\"srv_diff_host_rate\",\"dst_host_count\",\"dst_host_srv_count\",\n",
    "    \"dst_host_same_srv_rate\",\"dst_host_diff_srv_rate\",\"dst_host_same_src_port_rate\",\n",
    "    \"dst_host_srv_diff_host_rate\",\"dst_host_serror_rate\",\"dst_host_srv_serror_rate\",\n",
    "    \"dst_host_rerror_rate\",\"dst_host_srv_rerror_rate\",\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('kddcup.csv', names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(494021, 42)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Divide by class \n",
    "df_normal = df[df['target'] == 'normal.']\n",
    "df_neptune = df[df['target'] == 'neptune.']\n",
    "df_back = df[df['target'] == 'back.']\n",
    "df_teardrop = df[df['target'] == 'teardrop.']\n",
    "df_satan = df[df['target'] == 'satan.']\n",
    "df_warezclient = df[df['target'] == 'warezclient.']\n",
    "df_ipsweep = df[df['target'] == 'ipsweep.']\n",
    "df_smurf = df[df['target'] == 'smurf.']\n",
    "df_portsweep = df[df['target'] == 'portsweep.']\n",
    "df_pod = df[df['target'] == 'pod.']\n",
    "df_nmap = df[df['target'] == 'nmap.']\n",
    "df_guess_passwd = df[df['target'] == 'guess_passwd.']\n",
    "df_buffer_overflow = df[df['target'] == 'buffer_overflow.']\n",
    "df_warezmaster = df[df['target'] == 'warezmaster.']\n",
    "df_land = df[df['target'] == 'land.']\n",
    "df_imap = df[df['target'] == 'imap.']\n",
    "df_rootkit = df[df['target'] == 'rootkit.']\n",
    "df_loadmodule = df[df['target'] == 'loadmodule.']\n",
    "df_ftp_write = df[df['target'] == 'ftp_write.']\n",
    "df_multihop = df[df['target'] == 'multihop.']\n",
    "df_phf = df[df['target'] == 'phf.']\n",
    "df_perl = df[df['target'] == 'perl.']\n",
    "df_spy = df[df['target'] == 'spy.']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.concat([df_normal, df_neptune, df_smurf], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.drop('num_outbound_cmds', axis=1, inplace=True)\n",
    "df.drop('is_host_login', axis=1, inplace=True)\n",
    "df['protocol_type'] = df['protocol_type'].astype('category')\n",
    "df['service'] = df['service'].astype('category')\n",
    "df['flag'] = df['flag'].astype('category')\n",
    "df['target'] = df['target'].astype('category')\n",
    "cat_columns = df.select_dtypes(['category']).columns\n",
    "df[cat_columns] = df[cat_columns].apply(lambda x: x.cat.codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.560535</td>\n",
       "      <td>1.428892</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.561570</td>\n",
       "      <td>1.404603</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.541429</td>\n",
       "      <td>1.380527</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.514420</td>\n",
       "      <td>1.354879</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.488805</td>\n",
       "      <td>1.329318</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   principal component 1  principal component 2  target\n",
       "0               0.560535               1.428892     1.0\n",
       "1               0.561570               1.404603     1.0\n",
       "2               0.541429               1.380527     1.0\n",
       "3               0.514420               1.354879     1.0\n",
       "4               0.488805               1.329318     1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df.values\n",
    "Y = data[:,39]\n",
    "X = data[:,0:39]\n",
    "Y = Y.reshape(-1, 1)\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sScaler = StandardScaler()\n",
    "rescaleX = sScaler.fit_transform(X)\n",
    "pca = PCA(n_components=2)\n",
    "rescaleX = pca.fit_transform(rescaleX)\n",
    "rescaleX = np.append(rescaleX, Y, axis=1)\n",
    "principalDf = pd.DataFrame(data = rescaleX, columns = ['principal component 1', 'principal component 2', 'target'])\n",
    "principalDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    280790\n",
       "0.0    107201\n",
       "1.0     97278\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principalDf.target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xca33429cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Aw9mp1O8YtBERJZhbUOU0hipIEJOmgIezU6nfMWgjIkowp6AqP5TDno1X2rbusAtushn7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xca33429080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(principalDf.iloc[:,0], principalDf.iloc[:,1])\n",
    "plt.title('Principal component 1 vs 2')\n",
    "plt.xlabel(\"pc 1\")\n",
    "plt.ylabel(\"pc 2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = principalDf.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "label = data[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    280790\n",
       "0.0    107201\n",
       "1.0     97278\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principalDf.target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names = ['neptune', 'normal', 'smurf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['neptune', 'normal', 'smurf']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(len(names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe434fd9b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uaTtIVXcAgKruEJG/czhnPADrquKt2WN5RGQugLkAMGnSpBIPlahC2AM2v+NR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe434fd9ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "plt.figure(figsize = (10, 6))\n",
    "#markers = ['o', 's']\n",
    "#label = numpy array of target column\n",
    "plt.title('KDD data set - Linear separability')\n",
    "plt.xlabel('pc1')\n",
    "plt.ylabel('pc2')\n",
    "for i in range(len(names)):\n",
    "    bucket = principalDf[principalDf['target'] == i]\n",
    "    bucket = bucket.iloc[:,[0,1]].values\n",
    "    plt.scatter(bucket[:, 0], bucket[:, 1], label=names[i]) \n",
    "plt.legend(loc='upper left',\n",
    "           fontsize=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe448963ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe448963d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UsTqshFmcZKU2EWHGjBn89ddfdO/enUKFClGzZk2rw1LKpeg9bUqpdMkYw5Il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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe43555e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.spatial import ConvexHull\n",
    " \n",
    "plt.clf()\n",
    "plt.figure(figsize = (10, 6))\n",
    "plt.title('KDD data set - Linear separability - Convex Hull')\n",
    "plt.xlabel('pc1')\n",
    "plt.ylabel('pc2')\n",
    "markers = ['+', 's', 'x']\n",
    "for i in range(len(names)):\n",
    "    bucket = principalDf[principalDf['target'] == i]\n",
    "    bucket = bucket.iloc[:,[0,1]].values\n",
    "    hull = ConvexHull(bucket)\n",
    "    plt.scatter(bucket[:, 0], bucket[:, 1], label=names[i], marker=markers[i]) \n",
    "    for j in hull.simplices:\n",
    "        plt.plot(bucket[j,0], bucket[j,1], color='#000000')\n",
    "plt.legend(loc='upper left',\n",
    "           fontsize=8)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
